Peer-Timo Bremer
Lawrence Livermore National Laboratory
Publications 206
Training deep neural networks on large scientific data is a challenging task that requires enormous compute power, especially if no pre-trained models exist to initialize the process. We present a novel tournament method to train traditional as well as generative adversarial networks built on LBANN, a scalable deep learning framework optimized for HPC systems. LBANN combines multiple levels of parallelism and exploits some of the worlds largest supercomputers. We demonstrate our framework by cre...
#1Rushil Anirudh (LLNL: Lawrence Livermore National Laboratory)H-Index: 5
Last.B. K. SpearsH-Index: 29
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There is significant interest in using modern neural networks for scientific applications due to their effectiveness in modeling highly complex, non-linear problems in a data-driven fashion. However, a common challenge is to verify the scientific plausibility or validity of outputs predicted by a neural network. This work advocates the use of known scientific constraints as a lens into evaluating, exploring, and understanding such predictions for the problem of inertial confinement fusion.
#1Shusen LiuH-Index: 9
Last.Jim GaffneyH-Index: 5
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With the rapid adoption of machine learning techniques for large-scale applications in science and engineering comes the convergence of two grand challenges in visualization. First, the utilization of black box models (e.g., deep neural networks) calls for advanced techniques in exploring and interpreting model behaviors. Second, the rapid growth in computing has produced enormous datasets that require techniques that can handle millions or more samples. Although some solutions to these interpre...
May 1, 2019 in ICASSP (International Conference on Acoustics, Speech, and Signal Processing)
#1Jayaraman J. Thiagarajan (LLNL: Lawrence Livermore National Laboratory)H-Index: 10
#2Rushil Anirudh (LLNL: Lawrence Livermore National Laboratory)H-Index: 5
Last.Peer-Timo Bremer (LLNL: Lawrence Livermore National Laboratory)H-Index: 27
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#1Attila GyulassyH-Index: 16
#2Peer-Timo Bremer (LLNL: Lawrence Livermore National Laboratory)H-Index: 27
Last.Valerio PascucciH-Index: 43
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Topological techniques have proven to be a powerful tool in the analysis and visualization of large-scale scientific data. In particular, the Morse-Smale complex and its various components provide a rich framework for robust feature definition and computation. Consequently, there now exist a number of approaches to compute Morse-Smale complexes for large-scale data in parallel. However, existing techniques are based on discrete concepts which produce the correct topological structure but are kno...